The rapid advancement of Large Language Models (LLMs) has transformed conversational systems into practical tools used by millions. However, the nature and necessity of information retrieval in real-world conversations remain largely unexplored, as research has focused predominantly on traditional, explicit information access conversations. The central question is: What do real-world information access conversations look like? To this end, we first conduct an observational study on the WildChat dataset, large-scale user-ChatGPT conversations, finding that users' access to information occurs implicitly as check-worthy factual assertions made by the system, even when the conversation's primary intent is non-informational, such as creative writing. To enable the systematic study of this phenomenon, we release the WildClaims dataset, a novel resource consisting of 121,905 extracted factual claims from 7,587 utterances in 3,000 WildChat conversations, each annotated for check-worthiness. Our preliminary analysis of this resource reveals that conservatively 18% to 51% of conversations contain check-worthy assertions, depending on the methods employed, and less conservatively, as many as 76% may contain such assertions. This high prevalence underscores the importance of moving beyond the traditional understanding of explicit information access, to address the implicit information access that arises in real-world user-system conversations.

翻译：大型语言模型（LLM）的快速发展已将对话系统转变为数百万人使用的实用工具。然而，真实对话中信息检索的本质与必要性在很大程度上仍未得到探索，因为现有研究主要集中于传统的、显式的信息获取对话。核心问题在于：真实世界的信息获取对话呈现何种形态？为此，我们首先对大规模用户-ChatGPT对话数据集WildChat展开观察性研究，发现用户对信息的获取以系统生成的可核查事实断言的形式隐式发生，即使对话的主要意图是非信息性的（例如创意写作）。为系统研究这一现象，我们发布了WildClaims数据集——一个包含121,905条从3,000段WildChat对话的7,587条话语中提取的事实断言的新型资源，每条断言均标注了可核查性。对该资源的初步分析表明，保守估计有18%至51%的对话包含可核查断言（具体比例取决于所采用的方法），非保守估计下这一比例可能高达76%。如此高的普遍性凸显了超越传统显式信息获取认知的重要性，亟需关注真实用户-系统对话中涌现的隐式信息获取问题。